Intro

In recent years, there has been growing concern about the adverse effects of traffic noise on mental health, particularly the risk of depression. Traffic noise is a pervasive environmental stressor that affects millions of people worldwide, and its detrimental effects on physical health have been well documented. However, the impact of traffic noise on mental health, specifically depression, is less well understood. Noise pollution is a serious health concern, and exposure to excessive noise has been linked to a range of physical and mental health problems, including cardiovascular disease, sleep disturbance, and anxiety. Traffic noise, in particular, is a significant contributor to noise pollution in urban areas, and its impact on mental health has become a subject of increasing research interest. Recent studies have suggested that traffic noise exposure is associated with an increased risk of depression, but the underlying mechanisms are not yet fully understood. Some researchers have proposed that traffic noise may increase the risk of depression by disrupting sleep and increasing stress levels, while others have suggested that it may affect the brain’s cognitive function and emotional regulation. Understanding the relationship between traffic noise and depression is essential for developing effective strategies to mitigate the adverse effects of noise pollution on mental health. Given the high prevalence of traffic noise exposure in urban areas and the significant burden of depression, a better understanding of the relationship between these two factors is crucial for public health. This paper aims to explore the existing literature on the correlation between traffic noise and depression, with a particular focus on the underlying mechanisms and the potential implications for public health. The findings of this review could inform policymakers and urban planners in developing effective strategies to reduce the impact of traffic noise on mental health and improve the overall quality of life in urban areas.

Litrature Review

Literature 0 - EPA Identifies Noise Levels Affecting Health and Welfare

The Environmental Protection Agency (EPA) has released a document titled “Information on Levels of Environmental Noise Requisite to Protect Public Health and Welfare with an Adequate Margin of Safety,” which identifies noise levels required to protect public health and welfare against hearing loss, annoyance, and activity interference. The document suggests that exposure to 70 decibels of noise over a 24-hour period will prevent measurable hearing loss over a lifetime, while levels of 55 decibels outdoors and 45 decibels indoors can prevent activity interference and annoyance. These noise levels are not peak levels but represent averages of acoustic energy over periods of time. The EPA document identifies different noise levels for different areas, depending on their use, with 45 decibels associated with indoor residential areas, hospitals, and schools, 55 decibels identified for certain outdoor areas where human activity takes place, and 70 decibels for all areas to prevent hearing loss. The information in the document can be used by state and local governments to set noise standards, but other relevant factors such as costs and benefits, local aspirations, and means available to control environmental noise must also be considered.

Literature 1 – Study of physio-psychological effects on traffic wardens due to traffic noise pollution; exposure-effect relation

Noise pollution is a growing problem in urban areas around the world, and Pakistan is no exception. In fact, it is often considered more dangerous than air and water pollution due to its direct acute and chronic physio-psychological effects. In this study, the objective was to analyze and evaluate the psychological and physiological effects caused by traffic noise on traffic wardens in two cities in Pakistan: Taxila and Islamabad. To conduct this study, three check posts near roads were selected for survey. The survey included noise measurements at the check posts for one month and interviews with traffic wardens using a Performa based questionnaire. The results showed that noise levels varied between 85-106 dB, violating OSHA regulations. The psychological effects found in the wardens included aggravated depression (58%), stress (65%), public conflict (71%), irritation and annoyance (54%), behavioral affects (59%), and speech interference (56%). The physiological effects found were hypertension (87%), muscle tension (64%), exhaustion (48%), low performance levels (55%), concentration loss (93%), hearing impairment (69%), headache (74%), and cardiovascular issues (71%). To determine the relationship between exposure time and the effects of traffic noise on the wardens, a simple regression test was conducted in Excel. The percentage of psychological and physiological effects in the wardens varied with the exposure time. For example, aggravated depression had an R2 value of 0.946 and a P value of 0.133, while hypertension had an R2 value of 0.96 and a P value of 0.00095. The rapid population increase, urbanization, and industrialization in Pakistan have led to a significant change in the economical and social structure of society. With advancements in transportation facilities and technology, traffic loads are increasing at an alarming rate, which initiates a major problem of noise pollution. Noise is an unwanted sound that has different frequencies and acoustic pressure without any regular pattern. The major causes of noise pollution are roads, railways, and air traffic. The characteristics of noise (frequency and acoustic pressure) depend on the characteristics of the traffic and road, such as road gradient, road surface type, surrounding topography, grade of road, number and type of vehicles, age of vehicles passing, speed of vehicles, type of goods transported, packing of goods in vehicles, horn sound pressure, meteorological conditions, type of brakes, and behavior of the drivers. The most effective way to control noise pollution is to reduce it at the source, such as the vehicle and road. Noise contribution from different parts of vehicles is different, with the air intake system contributing 9%, the exhaust system contributing 27%, the tires contributing 30%, and the engine contributing 34%. At a speed of 70 km/h, tire noise dominates other sources within the vehicle. Therefore, limiting the vehicle speed on busy roads substantially reduces the noise level. Reducing the gradient of the road by only 5% can reduce the Leq noise by 1.5 dB. The road surface macro-texture wavelength range should be 2-10-20 mm to reduce noise levels. Similarly, green belts, plants, and trees along the roads are effective in reducing noise levels. Mounds and barriers along the roads are suggested to construct for a 10 dB reduction. Building treatment is also effective for the reduction of internal noise, such as single-glazed and double-glazed windows that reduce noise levels by 10 and 25 dB, respectively. Management of noise pollution is important to avoid its hazardous effects. High noise levels deteriorate the health of people and lead to economic losses. Access to a pollution-free environment is the central theme of human rights. Therefore, it is everyone’s responsibility.

Literature 2 - Noise Pollution-Sources, Effects and Control

The article starts by discussing the sources of noise pollution, which can be natural or human-made. Natural sources include thunderstorms, earthquakes, and animal sounds, while human-made sources include transportation noise (e.g. cars, trains, airplanes), industrial noise (e.g. machinery, construction), and recreational noise (e.g. concerts, sporting events). The authors then go on to describe the effects of noise pollution on human health and well-being. Exposure to excessive noise can lead to a range of negative health outcomes, including hearing loss, sleep disturbance, cardiovascular disease, and mental health problems such as anxiety and depression. The effects of noise pollution can be particularly detrimental for vulnerable populations such as children, the elderly, and those with pre-existing health conditions. The article also discusses the economic impact of noise pollution, including lost productivity and healthcare costs. Additionally, the authors note that noise pollution can have negative impacts on wildlife, disrupting animal communication and causing habitat loss. Finally, the article discusses strategies for controlling noise pollution. These include measures such as noise barriers, sound insulation, and the use of quieter technologies. The authors emphasize the importance of both individual and collective action in addressing noise pollution, noting that changes in personal behavior and public policy can both play a role in reducing noise levels. Overall, the article provides a comprehensive overview of the sources, effects, and control of noise pollution, highlighting the importance of addressing this pervasive and often overlooked environmental issue.

Literature 3 - Noise pollution: non-auditory effects on health

This article was written in 2003 by Stansfeld & Matheson in 2003 and studied the non auditory effects noise pollution have on our health, it starts of by saying that continuous exposure to noise of 85-90 dBA over a lifetime in industrial settings, can lead to a progressive loss of hearing. However, the levels of environmental noise, as opposed to industrial noise, are much lower and effects on non-auditory health cannot be explained as a consequence of sound energy. Much like the other articles, the main conclusion here is that noise pollution main effect is annoyance which in return can cause stress responses, then symptoms and possibly illness and thus affect our health indirectly. However, noise may influence health directly and not through annoyance. The response to noise may depend on characteristics of the sound, including intensity, frequency, complexity of sound, duration, and the meaning of the noise. Regarding mental disorder, it found no relationship between aircraft noise and road traffic noise to psychiatric disorder even after adjustment for socio-demographic factors and baseline psychiatric disorder, although there was a small non-linear association of noise with increased anxiety scores.
Some studies in Japan found a correlation between exposure to higher levels of military aircraft noise to depressiveness and nervousness, and road traffic noise has been weakly associated with mental health symptoms after adjusting for age, sex, income, and length of residence, Overall, environmental noise seems to be linked to psychological symptoms but not to clinical psychiatric disorder. However, there may be a link to psychiatric disorder at much higher noise levels. In the subject of physical health, the results were similar and included: increasing blood pressure, cardiovascular diseases, and insomnia.

Literature 4 - Mental health effects of education

The paper explores the relationship between education and mental health in the context of Zimbabwe. The authors use an instrumental variable (IV) approach to estimate the causal effect of schooling on later life mental health. The IV used in this study is age-specific exposure to an educational reform in Zimbabwe in the 1980s. Before 1980, Zimbabwe was under British colonial rule, and there were several discriminatory policies that restricted educational attainment among Black Zimbabweans. After independence, the new government implemented reforms that benefited Black children who were of primary school-going age at the time. Since Zimbabwean kids started school when they turned six, those who were 13 years or younger in 1980 disproportionately benefited from these policies (treatment group). The partially treated group included some children who were 14 or 15 years of age in 1980 and experienced some educational gains. Older individuals (16 years or above at the time) were significantly less likely to experience any such benefits and thus form the control group. The authors find that the treated group eventually gained about three years of education and were 39% points more likely to attend secondary school. The IV results suggest that this enhanced education led to better mental health later in life. An extra year of schooling reduced the probability of reporting any symptoms related to depression (11.3%) or anxiety (9.8%) in adult life, and it also led to a decline in the severity of symptoms of both depression (6.1%) and anxiety (5.6%). The effects of education on mental health are larger among women and rural residents. The authors also find evidence that improved physical health, better health-related behavior, and an increase in female empowerment might be some of the mechanisms through which education might have shaped mental health in the Zimbabwean context. The findings of this paper contribute to two separate strands of literature. First, it adds to the growing evidence on the link between education and mental health. While some studies have found a positive relationship between education and mental health, others have found mixed results. This paper provides robust evidence of the causal effect of education on mental health, using a unique setting and a rigorous empirical approach. Second, the paper highlights the importance of investing in education in low-income countries as a means of improving mental health outcomes. Mental health is stigmatized in low-income countries, and under-treatment is prevalent. Improving educational outcomes can lead to better mental health, as well as other positive socio-economic outcomes. The paper has several policy implications. First, it underscores the need for governments to invest in education as a means of improving mental health outcomes. Second, it suggests that policies aimed at reducing gender and rural-urban disparities in education can have important mental health benefits. Third, it highlights the need to reduce the stigma associated with mental health issues in low-income countries, which can discourage people from seeking treatment. Finally, it provides evidence that improving physical health and health-related behavior can have positive spillover effects on mental health outcomes. Overall, the paper demonstrates the importance of investing in education as a means of improving mental health and other socio-economic outcomes.

Literature 5 - Income inequality and depression: a systematic review and meta‐analysis of the association and a scoping review of mechanisms

The study aimed to explore the association between income inequality and depression, one of the most prevalent mental health conditions globally. The researchers conducted a systematic review and meta-analysis of the existing literature on depression and income inequality, and also conducted a scoping review to identify potential mechanisms underlying this association. The search strategy for the systematic review included several databases such as PubMed/Medline, EBSCO, and PsycINFO, and the search string used was “(depress* OR mental) AND (inequal* OR Gini)”. The search was limited to studies published in English between January 1, 1990, and July 31, 2017, and involving human subjects. The reference lists of all included studies were also hand-searched for additional relevant reports or key terms. The inclusion criteria were studies providing primary quantitative data with a measure of depression or depressive symptoms as an outcome and any measure of income inequality at any geographical scale. Exclusion criteria were unpublished data, qualitative studies, and publications reporting duplicate data from the same population. After screening the titles and abstracts and obtaining full-text articles for relevant studies, the researchers conducted quality assessments using the Systematic Appraisal of Quality in Observational Research (SAQOR) tool. The SAQOR comprises six domains related to sample, exposure/outcome measurements, confounders, and reporting of data. The findings of the study showed a significant positive association between income inequality and depression prevalence, with a pooled effect estimate of 1.16 (95% CI: 1.07–1.25) in the meta-analysis. The results were consistent across different geographical scales, such as country-level, state/province-level, and neighborhood-level income inequality. The scoping review of potential mechanisms underlying the association between income inequality and depression identified several pathways, such as social comparison and status anxiety, psychosocial stress, lack of social cohesion and support, reduced social mobility, and increased exposure to environmental toxins and pollutants. The authors proposed a theoretical framework that integrates these pathways and highlights the role of social determinants of health and policies that influence the distribution of income and wealth in shaping the association between income inequality and depression. The study has several implications for policy and practice. The findings suggest that reducing income inequality may have positive effects on depression prevention and treatment, particularly among vulnerable populations such as women and people living in poverty. The study also highlights the importance of addressing the social determinants of health, such as education, housing, and employment, in promoting mental health and reducing health disparities. The authors recommend further research to elucidate the causal mechanisms underlying the association between income inequality and depression, and to develop and evaluate interventions that target these mechanisms.

Literature 6 - Relationship Between Long Working Hours and Depression

This article discusses the relationship between long working hours and depressive symptoms among clerical workers. The article begins by highlighting how depressive state/major depressive disorder is a major occupational health issue in developed countries, and many studies have evaluated the relationship between high job demand and/or low job control and various states of mental and physical health. The article then focuses on the relationship between long working hours and various states of mental and physical health, with several studies suggesting a negative effect. However, the results have not been consistent, and few studies have evaluated the long-term effects. The article discusses recent studies that have found an increased risk of new depressive symptoms/major depressive episodes among those with long working hours compared with those who work 7 to 8 hours per day. However, the understanding of the long-term effects of long working hours is still limited. Therefore, the purpose of this prospective study was to clarify how long working hours affect future depressive states and investigate how individuals who are long-hours overworked exhibit an increased risk of future depression. The study was conducted using unlinkable anonymous data collected from self-administered questionnaires. The article provides details of the research design and study populations, including the longitudinal study using repeated measurements of variables at all four time points spanning the three years of follow-up. The study found that long working hours increase the risk of future depressive states, and individuals who are long-hours overworked exhibit an increased risk of future depression. The article concludes by discussing the implications of the findings for potential steps to reduce the risk of major depression in employees.

Methodology

My 3 main data sets are:

1)Noise Pollution - In 2002 the Environmental Noise Directive was established in Europe and required all 27 member states to publish a noise report for every city with over 100,000 inhabitants every 5 years. The reports will be divided into 5 levels of noise: 55 to 59 dB, 60 to 64 dB, 65 to 69 dB, 70 to 74 dB and >75 dB under each noise level there will be the number of people exposed to it. The decibels are the mean for the whole day (7:00-19:00), for noise during evening hours (19:00-23:00) there is a 5 dB additional penalty and for night noise (23:00-7:00) there is a 10 dB additional penalty. The most recent data are from 2017 and included all cities in Europe with over 100,000 inhabitants from 36 different countries, because the END requires only member states to publish the report much of my data had NA’s values, after cleaning those values I was left with cities from 30 different countries. This data and information was gathered from the European Environment Agency site.

2)Mental Health Disorders - The second data I needed are ones of mental health disorders, including: depression, Anxiety disorders, Bipolar disorder, Eating disorders and Schizophrenia. This data was gathered from Our World in Data, and are the percentages of the population that suffer from each disorder. Because I had noise pollution data on a city level I opted to get data about mental health also on a city level, this data proved to be immensely difficult to find and only existed in the country level. To overcome this discrepancy I decided to group my noise pollution data by countries and sum the number of people exposed to each noise level, this had two main problems:

3)The third and last data will consist of indices that are believed to have a correlation to mental health disorders rates:

Mean years of schooling (MYS) - this index refers to the average number of years of education completed by individuals in a certain population. It is a measure of the level of education in a society or country. To calculate mean years of schooling, the total number of years of education completed by a group of individuals is divided by the total number of individuals in the group. For example, if a group of 100 individuals has a total of 1,000 years of education completed among them, then the mean years of schooling for that group is 10 years. Mean years of schooling can be used to compare the level of education across different groups, such as men and women or different regions of a country. It is also used as an input for various social and economic analyses, such as the calculation of the Human Development Index (HDI) by the United Nations Development Programme (UNDP).

The mean years of schooling was collected from “Global_Data_Lab”.

GINI Index - The Gini index, or Gini coefficient, is a statistical measure used to represent the distribution of income or wealth within a population. It is named after the Italian statistician Corrado Gini, who developed the concept in 1912. The Gini index is a number between 0 and 1, where 0 represents perfect equality (where everyone has the same income or wealth) and 1 represents perfect inequality (where one person has all the income or wealth and everyone else has none). To calculate the Gini index, the Lorenz curve is first plotted, which shows the cumulative share of the population’s income or wealth against the cumulative share of the population. The Gini index is then calculated as the area between the Lorenz curve and the line of perfect equality (the diagonal line from the bottom left corner to the top right corner of the graph), divided by the total area under the line of perfect equality. The Gini index is often used as a measure of income or wealth inequality in a society or country. Higher Gini coefficients indicate greater inequality, while lower Gini coefficients indicate greater equality. The index can be used to compare income or wealth distributions across different countries or over time within a single country.

The GINI index data was collected from “The World Bank” site.

The last data that is believed to have an effect on depression is work hours - this data was retrieved from Our World in Data and contained the annual mean work hours of each country in Europe in 2017, According to Eurostat, the average annual work hours per employed person in the European Union (EU-28) in 2017 was 1,569 hours. However, this figure can vary significantly by country and may be influenced by factors such as labor laws, collective bargaining agreements, and cultural norms. It’s worth noting that the EU-28 refers to all 28 member states of the European Union at the time, including the United Kingdom, which has since left the EU.

Limitations: It’s important to note that I started the data collecting process with 36 countries but ended up with 27 countries due to no data available at some of the countries, the countries that were removed from the research are:

The country that remained were the following: Austria, Belgium, Bulgaria,Croatia, Denmark, Estonia, Finland, France, Germany, Hungary, Iceland, Ireland, Italy, Latvia, Lithuania, Luxembourg, Netherlands, Norway, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, Switzerland and United Kingdom.

Appendices:

I used ArcMap to map out the noise pollution levels in Europe, in this map a lighter color means that fewer people exposed to noise pollution (above 55 dB) and darker color means that more people are exposed to noise pollution.

This map displays the percentages from the measured population that is exposed to 55+ dB:

This map displays the percentages of the population that is exposed to 55+ dB:

This map displays the percentages of the population that is suffering from depression:

This map displays the percentages of the population that is suffering from anxiety:

This map displays the percentages of the population that is suffering from eating disorders:

This map displays the percentages of the population that is suffering from bipolar disorder:

This map displays the percentages of the population that is suffering from Schizophrenia:

This map displays the Gini index of the EU countries:

This map displays the avreage years in school for every country:

This map displays the avreage annual working hours for every country: